NiO–CuO microsheets were fabricated via laser ablating a CuO target in NiSO₄ solutions for detecting ppb-level H₂S concentrations at room temperature (RT, 26 °C). The NiO-CuO composite product obtained in a 1 M NiSO₄ solution (1-NiO-CuO#) displayed a two-dimensional microsheet morphology derived from stacked nanosheets, yielding a large specific surface area. Additionally, the introduction of NiO triggered the formation of oxygen vacancies and imparted catalytic activity for H₂S oxidation. Consequently, the 1-NiO-CuO# sensor exhibited the detection limitation of 1 ppb H₂S at room temperature, while demonstrating high selectivity. Furthermore, the pulse-heating modulation method (low temperature: 26 °C, high temperature: 300 °C, cycle period: 30 s, heating duration: 4 s) was employed to effectively address the limitations the limitations of incomplete recovery and poor humidity resistance of the 1-NiO-CuO# sensor. The 1-NiO-CuO# sensor exhibited a fourfold response and a faster response time to 1 ppb H₂S under pulse-heating condition, compared with the results obtained at room temperature. The response and recovery times of the sensor to 10 ppb H₂S are respectively 330 s and 233 s under pulse-heating condition. Analysis of the O 1s X-ray photoelectron spectra of 1-NiO-CuO# before and after pulse-heating treatment suggests that heating pulse exerted a precleaning effect on oxygen vacancies prior to room-temperature sensing during the pulse-off period.

To improve the concentration prediction of ternary gas mixtures containing H2, CO, and CH4, a sensing framework combining heterogeneous sensors, composite temperature modulation, and deep-learning-based signal analysis is proposed. Two self-developed SnO2-based composite sensors fabricated by magnetron sputtering were integrated with commercial MEMS gas sensors to construct a heterogeneous sensor array. A composite heating waveform consisting of multiple thermal modulation stages was employed to generate diverse transient response characteristics under dynamic operating conditions. During the experiments, observable phase-shift behavior of transient response peaks was found under different gas concentration combinations, where characteristic peaks appeared earlier or later depending on the mixture composition. Based on this phenomenon, Discrete Wavelet Transform (DWT) was introduced to extract multi-scale transient features from the non-stationary sensing signals. The subsequent analysis indicated that several intermediate-frequency sub-bands preserved the transient phase-variation characteristics more effectively than low- or high-frequency components. These extracted features were further processed using a one-dimensional convolutional neural network (1D-CNN) for ternary gas concentration regression. Experimental results showed that the proposed framework achieved coefficients of determination (R2) above 0.95 for H2, CO, and CH4 concentration prediction. Comparative experiments further demonstrated that the combination of composite waveform modulation and DWT-based feature extraction improved the regression performance compared with conventional single-waveform modulation and several alternative signal-processing methods. The proposed method provides a potential approach for lightweight multi-component gas analysis under dynamic temperature modulation conditions.

Gas sensors based on metal oxide semiconductors (MOS) have attracted significant attention in monitoring of methane emission and leakage monitoring due to their high sensitivity, fast response time, simple structure and low cost. However, the high power consumption caused by long-term high-temperature operation of MOS sensors restricts their application in mobile and portable devices. In this study, MOF-derived Co3O4 dodecahedrons for low-concentration methane detection at room temperature was prepared using Zeolitic Imidazolate Framework-67 (ZIF-67) as a template and with various calcination temperatures. Among them, the Co3O4-350 calcined at 350 °C exhibited the optimal CH4 sensing performance at room temperature, with a response of Rg/Ra = 1.53 to 2000 ppm CH4. This enhanced gas sensing performance is attributed to the highest Co3+ proportions and the largest specific surface area in Co3O4-350 nanomaterials, which provided more active sites for gas adsorption and reaction. To address the challenge of slow response speed and irrecoverability during CH4 detection at room temperature, the Co3O4 nanomaterials were printed onto a micro-heater plate (MHP) to form a MEMS gas sensor. By introducing a pulse heating mode to the MEMS sensor, the response and recovery time were significantly reduced to 26 s and 21 s, respectively. This enhancement improves both the efficiency and reliability of the MEMS gas sensor for early-stage detection of CH4 leaks in various industrial applications.

To circumvent the inherent cross-sensitivity limitations in the high-fidelity quantification of ternary gas mixtures comprising hydrogen (H2), carbon monoxide (CO), and methane (CH2), a physics-data dual-driven framework is proposed, integrating nanostructured sensing materials, multi-mode thermal modulation, and advanced deep learning architectures. Specifically, novel SnO2-based composite nanomaterial sensors were synthesized via magnetron sputtering and synergistically integrated with commercial MEMS units to construct a heterogeneous sensor array, thereby enriching the multidimensional feature space at the hardware level. To elicit distinctive gas-sensitive kinetics, a composite waveform heating strategy was implemented to drive the array through complex non-equilibrium thermal profiles, effectively generating differentiated transient response patterns for distinct gas components. For robust signal decoding, Discrete Wavelet Transform (DWT) was utilized for multi-scale feature extraction, enabling the decoupling of molecular kinetic fingerprints by mapping distinct relaxation time constants into specific frequency sub-bands. Subsequently, an end-to-end regression model based on a One-Dimensional Convolutional Neural Network (1D-CNN) was developed and validated for precise concentration prediction. The proposed methodology achieved exceptional regression accuracy, with coefficients of determination (R2) exceeding 0.95 for all target analytes. This work establishes a holistic paradigm for the realization of low-cost, high-performance, and intelligent multi-component gas analysis systems.

Efficient particle enrichment underpins microfluidic clinical diagnostics, yet many biologically relevant samples are non-Newtonian and their behaviour in acoustic-oscillatory systems remains incompletely understood. Here, we compare particle-enrichment kinetics in Newtonian glycerol-water (GW), viscoelastic poly(ethylene oxide) (PEO), and shear-thinning xanthan gum (XG) solutions using a common acoustic-oscillatory microfluidic platform. The enrichment rate followed the order PEO > water > XG > GW under representative conditions. At fac = 1 MHz, Vpp = 17 V, and fflow = 12 Hz, 1.4 wt% PEO reduced the enrichment time by approximately 60% relative to water. Increased hydrodynamic resistance accounts for the slower response in GW, whereas shear thinning lowers the apparent viscosity of XG as the deformation rate increases. The faster response in PEO suggests an additional viscoelastic contribution. This contribution may assist migration toward the central node at 1 MHz but oppose migration locally between the centreline and the two off-centre nodes at 2 MHz. The voltage-dependent enrichment rate was described empirically by 1/te = aVbpp. Phase-resolved simulations based on the measured rheology and channel geometry showed fluid-dependent velocity, shear-rate, and apparent-viscosity fields. Fluid rheology must therefore be considered when designing acoustic-oscillatory enrichment systems for complex samples.

Electronic-nose (E-nose) screening of exhaled volatile organic compounds (VOCs) offers a promising route to non-invasive lung cancer detection, yet its deployment is constrained by redundant sensor arrays and inadequate modeling of discriminative temporal dynamics. To address both challenges, a hardware-algorithm co-design framework is presented. First, a compact MEMS sensor subset is selected from 18 candidates using a combined criterion of Pearson correlation and discriminant ability, reducing redundancy while preserving biomarker separability. Second, a Positional Relation Matrix (PRM) encoding is proposed, converting 1D response sequences into 2D topological representations, thus enabling a PRM-CNN model to capture global temporal dependencies beyond conventional 1D feature learning. On a clinical breath dataset comprising 106 lung cancer patients and 56 healthy controls, the E-nose consisting of 5 gas sensors and PRM-CNN model achieved 100% sensitivity, 84.6% specificity, and 93.9% accuracy on an independent test set.

Hierarchical CunO n O nanoflowers were synthesized through the laser ablation of a CuO target in NaOH solutions for room-temperature (27 degree celsius) H2S 2 S detection. Notably, the pH value of NaOH solutions influenced both the micro- morphologies and compositions of the CunO n O products, as evidenced by XRD, XPS, SEM and TEM. In high pH solutions, the specific surface area of the CunO n O products increased, their thickness decreased, and the Cu2O 2 O content diminished, resulting in enhanced sensitivity, selectivity and stability of the CunO n O products' response to H2S. 2 S. Notably, the pH14# # sample synthesized using an NaOH solution with a pH value of 14 featured pure CuO nanoflowers comprising slightly curled nanosheets with a thickness of approximately 10 nm. This sensor demonstrated excellent H2S 2 S sensing performance at room temperature, exhibiting a response value of 1.17 for 10 ppb H2S, 2 S, along with high selectivity and good long-term stability. However, after exposure to H2S, 2 S, the resistance of the sensor did not recover to its baseline in air at room temperature. Thermogravimetry results revealed that a temperature of 300 degrees C was effective for recovery of the sensor. Consequently, the operation temperature of the pH14# # sensor was controlled using a micro-hotplate. In the pulse heating mode, the sensor's response to 100 ppb H2S 2 S was 1.5, with a response time of 135 s and a recovery time of 137 s.

This study presents an efficient approach for the precise detection of chlorine gas (Cl-2) and hydrogen chloride (HCl), harmful pollutants frequently emitted from chlor-alkali and various industrial processes. These substances, even in trace amounts, pose significant health risks. Ion mobility spectrometry (IMS), known for its sensitivity in pollutant detection, traditionally struggles to differentiate between Cl-2 and HCl due to the similarity of their product ions, Cl-. To overcome this limitation, we introduce a novel technique combining dopant-assisted negative photoionization ion mobility spectrometry (DANP-IMS) with an automatic semiconductor cooling system. This unique combination utilizes the differential cryogenic removal efficiencies of Cl-2 and HCl to segregate these gases before analysis. By applying DANP-IMS, we achieved selective measurement of Cl- ion signal intensities under both standard and cryogenic conditions, facilitating the accurate quantification of total chlorine and Cl-2 levels. We then determined HCl concentrations by deducting the Cl-2 signal from the total chlorine readings. Our approach demonstrated detection limits of 2.0 parts per billion (ppb) for Cl-2 and 0.8 ppb for HCl, across a linear detection range of 0-200 ppb. Moreover, our method's capability for real-time atmospheric monitoring of Cl-2 and HCl near industrial sites underscores its utility for environmental monitoring, offering a robust solution for the separate and precise measurement of these pollutants.

Anti-drift is a new and serious challenge in the field related to gas sensors. Gas sensor drift causes the probability distribution of the measured data to be inconsistent with the probability distribution of the calibrated data, which leads to the failure of the original classification algorithm. In order to make the probability distributions of the drifted data and the regular data consistent, we introduce the Conditional Adversarial Domain Adaptation Network (CDAN)+ Sharpness Aware Minimization (SAM) optimizer—a state-of-the-art deep transfer learning method.The core approach involves the construction of feature extractors and domain discriminators designed to extract shared features from both drift and clean data. These extracted features are subsequently input into a classifier, thereby amplifying the overall model’s generalization capabilities. The method boasts three key advantages: (1) Implementation of semi-supervised learning, thereby negating the necessity for labels on drift data. (2) Unlike conventional deep transfer learning methods such as the Domain-adversarial Neural Network (DANN) and Wasserstein Domain-adversarial Neural Network (WDANN), it accommodates inter-class correlations. (3) It exhibits enhanced ease of training and convergence compared to traditional deep transfer learning networks. Through rigorous experimentation on two publicly available datasets, we substantiate the efficiency and effectiveness of our proposed anti-drift methodology when juxtaposed with state-of-the-art techniques.

There have been many studies on the significant correlation between the hydrogen peroxide content of different tissues or cells in the human body and the risk of disease, so the preparation of biosensors for detecting hydrogen peroxide concentration has been a hot topic for researchers. In this paper, palladium nanoparticles (PdNPs) and laser-induced graphene (LIG) were prepared by liquid-phase pulsed laser ablation and laser-induced technology, respectively. The complexes were prepared by stirring and used for the modification of screen-printed electrodes to develop a non-enzymatic hydrogen peroxide biosensor that is low cost and mass preparable. The PdNPs prepared with anhydrous ethanol as a solvent have a uniform particle size distribution. The LIG prepared by laser direct writing has good electrical conductivity, and its loose porous structure provides more adsorption sites. The electrochemical properties of the modified electrode were characterized by cyclic voltammetry, chronoamperometry, and electrochemical impedance spectroscopy. Compared with bare screen-printed electrodes, the modified electrodes are more sensitive for the detection of hydrogen peroxide. The sensor has a linear response range of 5 µM–0.9 mM and 0.9 mM–5 mM. The limit of detection is 0.37 µM. The above conclusions indicate that the hydrogen peroxide electrochemical biosensor prepared in this paper has great advantages and potential in electrochemical catalysis.

Pure SnO2 and 1 at.% PdO–SnO2 materials were prepared using a simple hydrothermal method. The micromorphology and element valence state of the material were characterized using XRD, SEM, TEM, and XPS methods. The SEM results showed that the prepared material had a two-dimensional nanosheet morphology, and the formation of PdO and SnO2 heterostructures was validated through TEM. Due to the influence of the heterojunction, in the XPS test, the energy spectrum peaks of Sn and O in PdO–SnO2 were shifted by 0.2 eV compared with SnO2. The PdO–SnO2 sensor showed improved ethanol sensing performance compared to the pure SnO2 sensor, since it benefited from the large specific surface area of the nanosheet structure, the modulation effect of the PdO–SnO2 heterojunction on resistance, and the catalyst effect of PdO on the adsorption of oxygen. A DFT calculation study of the ethanol adsorption characteristics of the PdO–SnO2 surface was conducted to provide a detailed explanation of the gas-sensing mechanism. PdO was found to improve the reducibility of ethanol, enhance the adsorption of ethanol’s methyl group, and increase the number of adsorption sites. A synergistic effect based on the continuous adsorption sites was also deduced.

The online monitoring of hydrogen sulfide (H2S) presents a critical advancement for environmental protection and public health, yet existing methodologies struggle to achieve on-site, on-line and highly sensitive detection in atmospheric conditions. This study introduces a novel approach employing ozone-enhanced photoionization ion mobility spectrometry (IMS) in conjunction with a time-resolved dynamic diluter (TRDD) for the efficacious identification of H2S within environments of high humidity, such as sewers. This method ingeniously addresses the challenge of distinguishing H2S in the presence of water vapor through a dual strategy of ozone oxidization pretreatment and TRDD injection. The strategic placement of ozone within the drift region significantly amplifies the sensitivity of the IMS detection, showcasing the method's superior sensitivity, stability, and humidity interference resilience. Notably, this technique achieves a detection limit for H2S at an impressive 2.5 parts per billion (ppb), maintaining a consistent relative standard deviation (RSD) of 2.5% over three consecutive days. Furthermore, the approach effectively mitigates the effects of moisture, maintaining an RSD of 3.0% despite a relative humidity (RH) increase from 22% to 95%. Applied to the on-site measurement of H2S in sewer systems, the method demonstrated all the aforementioned performance characteristics, marking it a promising way of high humidity environment H2S monitoring.

In this study, we report a high-performance acetone gas sensor utilizing a bilayer structure composed of a ZnO nanorod top layer and a ZnFe2O4 nanoparticle-decorated ZnO nanorod bottom layer. ZnO nanorods were synthesized via a water-bath method, after which the ZnFe2O4 nanoparticle-decorated ZnO nanorods were prepared using a simple immersion and calcination method. SEM and TEM revealed the porous morphology of the samples and the formation of ZnO-ZnFe2O4 heterojunctions. XPS analysis demonstrated an increase in oxygen vacancy content with the introduction of ZnFe2O4 nanoparticles. Compared to pure ZnO nanorods, ZnFe2O4-decorated ZnO nanorods showed a 3.9-fold increase in response to 50 ppm acetone. Covering this layer with ZnO nanorods further increased the response by an additional 1.6 times, and simultaneously enhanced the selectivity to acetone. The top layer improves gas sensing performance by introducing heterojunctions with the bottom layer, partially blocking acetone gas at the bottom layer to facilitate a more complete reaction, and filtering ethanol interference.

Owing to the separation of field-effect transistor (FET) devices from sensing environments, extended-gate FET (EGFET) biosensor features high stability and low cost. Herein, a highly sensitive EGFET biosensor based on a GaN micropillar array and polycrystalline layer (GMP) was fabricated, which was prepared by using simple one-step low-temperature MOCVD growth. In order to improve the sensitivity and detection limit of EGFET biosensor, the surface area and the electrical conductivity of extended-gate electrode can be increased by the micropillar array and the polycrystalline layer, respectively. The designed GMP-EGFET biosensor was modified with l-cysteine and applied for Hg2+ detection with a low limit of detection (LOD) of 1 ng/L, a high sensitivity of -16.3 mV/lg(μg/L) and a wide linear range (1 ng/L-24.5 μg/L). In addition, the detection of Hg2+ in human urine was realized with an LOD of 10 ng/L, which was more than 30 times lower than that of reported sensors. To our knowledge, it is the first time that GMP was used as extended-gate of EGFET biosensor.

Ethanol and acetone sensors have a wide variety of applications across different industries. However, it is necessary to improve the performance of such sensors and to understand the underlying mechanisms. In this study, Pd/PdO-WO3 nanoblocks are synthesized via hydrothermal growth and calcination. The Pd and PdO contents of the materials are tuned by varying the Pd doping concentration and annealing temperature. The gas sensing performance of the nanoparticles is investigated, which shows that Pd doping increases the sensitivity and reduces the optimum operating temperature. At 200 degrees C, Pd/PdO-WO3 nanoblocks with different PdO ratios exhibit good sensitivity to acetone and ethanol. However, because PdO is an active catalyst for ethanol oxidation, the oxygen sensitivity increases as the PdO ratio increases. The prepared sensors exhibit good stability and excellent selectivity against a variety of interferents, and the detection limit of the target gas is 100 ppb. The chemical and electronic sensitization of Pd/PdO lowers the activation barrier and improves the gas sensing response. This study demonstrates that the selectivity of a gas sensor can be regulated by controlling the electronic states of the active species.

For deposition of two-dimensional materials (e.g., graphene) on a substrate, self-aggregation and poor anchor strength are still issues. Herein, the GaN nanowire (NW) substrate was employed for electrochemical deposition of reduced graphene oxide (rGO) with satisfying dispersion uniformity and anchor strength. The deposited rGO exhibited flake morphology without agglomeration. Moreover, PtAu and rGO can be simultaneously and uniformly deposited on the GaN NW substrate to realize a PtAu–rGO/GaN electrochemical sensor for glucose detection. In comparison with deposition of PtAu–rGO on a stainless steel (SS) substrate (i.e., PtAu–rGO/SS), PtAu–rGO/GaN demonstrated much higher sensitivity and long-term stability, owing to better dispersion and anchor strength on GaN NW. In addition, with decoration of glucose oxidase (GOx), the GOx/PtAu–rGO/GaN sensor can be used for detecting glucose in human sweat with a low limit of detection of 5 μM, a wide linear detection range of 5 μM–12 mM, and high long-term stability, which indicates that GOx/PtAu–rGO/GaN sensor is promising for noninvasive glucose detection.

A sensor array comprising multiple gas sensors can improve the poor selectivity of semiconductor gas sensors but increases the system size and power consumption. To overcome these limitations, we propose an ultra-high-integrated and highly selective electronic nose (E-nose) comprising two independent gas-sensing elements on a low-power microhotplate (MHP). Pd-SnO 2 nanoflowers and Pd-WO 3 microparticles were prepared and printed on a bridge-structured MHP 20 µm apart and at an area of 110 µm × 55 µm using electrohydrodynamic inkjet printing aided by in-situ infrared laser curing. It takes only 17 mW for the MHP to heat to 300 °C, which is the optimal operating temperature for the two gas sensors to achieve high response and low cross-sensitivity to hydrogen, ammonia, hydrogen–ammonia, ethanol, acetone, ethanol–acetone, toluene, and formaldehyde. Wavelet transform reduces the noise and dimensionality of gas-sensor array signals. Qualitative identification of the eight gases with an accuracy of 99.86% was achieved using the k -nearest neighbor ( k NN) model. A p neighbors back propagation neural network ( p N-BPNN) model was established to remove interfering samples to quantitatively estimate the gas concentration. The quantitative identification accuracy of p N-BPNN was higher than that of basic back propagation neural networks (BPNN) model with the average absolute percentage error of hydrogen in the range of 15–500 ppm decreasing from 5.44% to 2.08%.

Metal oxide (MOX) gas sensor arrays play an important role in various fields of gas detection, but their development is also limited by their performance deficiencies, such as measurement delays due to slow response times, and cross sensitivity interfering with gas identification. In addition, gas identification typically requires complete time series data of the steady-state response and reaction of the sensor array, which affects the efficiency. In this article, we propose a novel algorithm transformer equipped temporal convolution network (TTCN) based on the transformer and temporal convolution network (TCN) structure that can automatically perform feature extraction and gas mixture recognition on time series data before reaching equilibrium, overcoming the recognition difficulties caused by measurement delays and measurement interferences. This algorithm extracts global and local features using the attention mechanism in the transformer structure and multiscale convolution in the TCN structure to acquire instantaneous information on changes in the trends of gases for improved gas identification. The TTCN provides precise identification of early gas data and identifies ternary mixtures of formaldehyde, ethanol, and acetone with an average identification accuracy of 98.23%. In this study, we carry out in-depth tests to confirm the efficacy of our proposed algorithm and to show its significant advantages over other algorithms. Importantly, the excellent identification performance of the TTCN in the early stages of gas exposure demonstrates its significance for future real-time applications.